TSGP uses a pre-trained transformer as a semantic variation operator in genetic programming, generalizes across d-dimensional problems, and outperforms standard GP, SLIM_GSGP, Deep Symbolic Regression, and Denoising Autoencoder GP on 24 datasets while producing more compact solutions.
David Wittenberg
2 Pith papers cite this work. Polarity classification is still indexing.
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Experiments reveal that cross-modal alignment in SNIP does not improve with increasing fitness and is too coarse for effective symbolic search in latent space optimization for symbolic regression.
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Transformer Semantic Genetic Programming for d-dimensional Symbolic Regression Problems
TSGP uses a pre-trained transformer as a semantic variation operator in genetic programming, generalizes across d-dimensional problems, and outperforms standard GP, SLIM_GSGP, Deep Symbolic Regression, and Denoising Autoencoder GP on 24 datasets while producing more compact solutions.
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Multi-Modal Learning meets Genetic Programming: Analyzing Alignment in Latent Space Optimization
Experiments reveal that cross-modal alignment in SNIP does not improve with increasing fitness and is too coarse for effective symbolic search in latent space optimization for symbolic regression.